Large Scale Mass Spectrometry-based Identifications of Enzyme-mediated Protein Methylation Are Subject to High False Discovery Rates

Gene Hart-Smith,Daniel Yagoub,Aidan P Tay,Russell Pickford,Marc R Wilkins

doi:10.1074/mcp.m115.055384

Abstract

All large scale LC-MS/MS post-translational methylation site discovery experiments require methylpeptide spectrum matches (methyl-PSMs) to be identified at acceptably low false discovery rates (FDRs). To meet estimated methyl-PSM FDRs, methyl-PSM filtering criteria are often determined using the target-decoy approach. The efficacy of this methyl-PSM filtering approach has, however, yet to be thoroughly evaluated. Here, we conduct a systematic analysis of methyl-PSM FDRs across a range of sample preparation workflows (each differing in their exposure to the alcohols methanol and isopropyl alcohol) and mass spectrometric instrument platforms (each employing a different mode of MS/MS dissociation). Through (13)CD3-methionine labeling (heavy-methyl SILAC) of Saccharomyces cerevisiae cells and in-depth manual data inspection, accurate lists of true positive methyl-PSMs were determined, allowing methyl-PSM FDRs to be compared with target-decoy approach-derived methyl-PSM FDR estimates. These results show that global FDR estimates produce extremely unreliable methyl-PSM filtering criteria; we demonstrate that this is an unavoidable consequence of the high number of amino acid combinations capable of producing peptide sequences that are isobaric to methylated peptides of a different sequence. Separate methyl-PSM FDR estimates were also found to be unreliable due to prevalent sources of false positive methyl-PSMs that produce high peptide identity score distributions. Incorrect methylation site localizations, peptides containing cysteinyl-S-β-propionamide, and methylated glutamic or aspartic acid residues can partially, but not wholly, account for these false positive methyl-PSMs. Together, these results indicate that the target-decoy approach is an unreliable means of estimating methyl-PSM FDRs and methyl-PSM filtering criteria. We suggest that orthogonal methylpeptide validation (e.g. heavy-methyl SILAC or its offshoots) should be considered a prerequisite for obtaining high confidence methyl-PSMs in large scale LC-MS/MS methylation site discovery experiments and make recommendations on how to reduce methyl-PSM FDRs in samples not amenable to heavy isotope labeling. Data are available via ProteomeXchange with the data identifier PXD002857.

Highlights

From the ‡New South Wales Systems Biology Initiative, School of Biotechnology and Biomolecular Sciences, and ࿣Bioanalytical Mass Spectrometry Facility, University of New South Wales, Sydney, New South Wales 2052, Australia
Methyl-peptide spectrum matches (PSMs) false discovery rates (FDRs) were determined for datasets produced via Percolator filtering using global FDR estimates and datasets produced from Mascot Ion Score thresholding
Where for datasets filtered to an estimated Ͻ1% global FDR via Percolator, TP ϭ the number of remaining non-redundant true positive methyl-PSMs, and P ϭ the number of remaining non-redundant methyl-PSMs

Summary

Introduction

From the ‡New South Wales Systems Biology Initiative, School of Biotechnology and Biomolecular Sciences, and ࿣Bioanalytical Mass Spectrometry Facility, University of New South Wales, Sydney, New South Wales 2052, Australia. We suggest that orthogonal methylpeptide validation (e.g. heavy-methyl SILAC or its offshoots) should be considered a prerequisite for obtaining high confidence methyl-PSMs in large scale LC-MS/MS methylation site discovery experiments and make recommendations on how to reduce methylPSM FDRs in samples not amenable to heavy isotope labeling. Post-translational methylation is a widespread protein modification, which predominantly occurs on lysine and arginine residues [1]. Systematic Analysis of Methylpeptide False Discovery Rates cated that methylation is widespread among non-histone proteins (4 –16). These studies have associated methylation with a diverse range of cellular processes, including RNA processing, DNA repair and splicing, translation, helicase activity, ATPase activity, and spindle assembly checkpoints [4, 17, 18]

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